On the other hand, we have planning-based predictors. These are based on the idea that human motion results from decisions people make in pursuit of their goals and preferences. Learn their goals and preferences, and leverage planning to generate the corresponding motions [1]}, [2]}, [3]}. Sticking to the braking example: this type of predictor will easily figure out that a human will need to brake without needing to see their brake lights first. It learns that people want to make progress but avoid collisions and stop at stop signs, and it uses a planner to generate a motion that will do just that. If slowing down is necessary for collision avoidance or because a stop sign is coming up, that is what the planner will do. And indeed, prior work has shown such predictors to be preferable in highly interactive domains, depending on how one collects their training data [4]}.
